Predictive Maintenance
Challenge
The Problem
The client operated 14 large rotating machines (fans, pumps, conveyors) across two production lines. Unexpected bearing failures caused an average of 3 unplanned stoppages per month, each costing 4–8 hours of lost production. Maintenance was purely reactive — no condition monitoring was in place.
Approach
Our Solution
We deployed wireless vibration and temperature sensors on all 14 assets, feeding data into our cloud IIoT platform. An ML model trained on 18 months of historical failure data was calibrated per asset. The model generates a failure probability score updated every 5 minutes, with push alerts when a score crosses the intervention threshold.
Outcomes
Results Achieved
↓ 40% reduction in unplanned downtime
↓ 62% reduction in reactive maintenance callouts
ROI achieved within 7 months of go-live
14 assets monitored continuously, 24 / 7
Business Impact
ROI Statement
Every avoided stoppage saves ~$12,000 in lost production. With an average of 2 avoided stoppages per month, annual savings exceed $280,000 — against a total project cost of $160,000.
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